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fuzzy c-means clustering method with the fuzzy distance definition applied on symmetric triangular fuzzy numbers

JOURNAL OF INTELLIGENT & fuzzy SYSTEMS 2020年第3期38卷 2891-2905页

作者： Hossein-Abad, Hadi Mahdipour Shabanian, Mohsen Kazerouni, Iman Abaspur Esfarayen Univ Technol Dept Elect Engn Esfarayen North Khorasan Iran Salman Farsi Univ Kazerun Dept Elect Engn Kazerun Fars Iran Univ Limerick CRIS Limerick Ireland

The conventional fuzzy c-means (FcM) clustering method can be applied on data, where data features are crisp;however, when the features are fuzzy, the conventional FcM cannot be utilized. Recently, some researchers applied FcM on fuzzy numbers when the used metric has a crisp value. Since difference between two fuzzy numbers can be represented by a fuzzy value better than crisp one, in this paper, it is going to extend the FcM method for clustering symmetric triangular fuzzy numbers, where the used metric has a fuzzy value. It will be shown that the proposed fuzzy distance expresses the distance between two fuzzy numbers much better than crisp metrics. Then the proposed method has been applied on simulated and various real data, where it is compared with several new methods. The experimental results show better performance of the proposed method in compare to other ones.

关键词： clustering fuzzy c-means clustering method triangular fuzzy number fuzzy smaller

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Evaluation of Water Inrush Risk in the Fault Zone of the coal Seam Floor in Madaotou coal Mine, Shanxi Province, china

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WATER 2025年第2期17卷 259-259页

作者： Yu, Shuai Ding, Hanghang Yang, Moyuan Zhang, Menglin Capital Normal Univ Coll Resource Environm & Tourism Beijing 100048 Peoples R China Beijing Water Sci & Technol Inst Beijing 100048 Peoples R China Chinese Acad Sci Expt Sch Affiliated Beijing 100020 Peoples R China

As coal seams are mined at greater depths, the threat of high water pressure from the confined aquifer in the floor that mining operations face has become increasingly prominent. Taking the Madaotou mine field in the Datong coalfield as the research object, in the context of mining under pressure, for the main coal seams in the mining area, first of all, an improved evaluation method for the vulnerability of floor water inrush is adopted for hazard prediction. Secondly, numerical simulation is used to conduct a simulation analysis on the fault zones in high-risk areas. By using the fuzzy c-means clustering method (FccM) to improve the classification method for the normalized indicators in the original variable-weight vulnerability evaluation, the risk zoning for water inrush from the coal seam floor is determined. Then, through the numerical simulation method, a simulation analysis is carried out on high-risk areas to simulate the disturbance changes of different mining methods on the fault zones so as to put forward reasonable mining methods. The results show that the classification of the variable-weight intervals of water inrush from the coal seam floor is more suitable to be classified by using fuzzy clustering, thus improving the prediction accuracy. Based on the time effect of the delayed water inrush of faults, different mining methods determine the duration of the disturbance on the fault zones. Therefore, by reducing the disturbance time on the fault zones, the risk of karst water inrush from the floor of the fault zones can be reduced. Through prediction evaluation and simulation analysis, the evaluation of the risk of water inrush in coal mines has been greatly improved, which is of great significance for ensuring the safe and efficient mining of mines.

关键词： fuzzy c-means clustering method vulnerability assessment floor water inrush graben

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Research on spectral clustering algorithm for network communication big data based on wavelet analysis

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INTERNATIONAL JOURNAL OF AUTONOMOUS AND ADAPTIVE cOMMUNIcATIONS SYSTEMS 2022年第2期15卷 93-105页

作者： Dai, Xinjian Zeng, Zhichao Changsha Social Work Coll Basic Data Applicat Teaching & Res Off Changsha 410004 Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China

In order to classify the features of big data in network communication, improve clustering efficiency and reduce error classification rate, a spectrum clustering algorithm based on wavelet analysis is proposed. The multi-scale, one-dimensional wavelet analysis method is used to sample the network communication big data, extract the spectral feature quantity of the network communication big data, and construct the channel model of big data transmission of network communication. combined with the fuzzy c-means clustering method, the spectral clustering is performed on network communication big data to mine association rules of the large data spectrum of network communication. combined with wavelet decomposition method, the time-frequency conversion and feature separation of network communication big data spectrum are carried out to complete the spectrum clustering of network communication big data. Simulation results show that this method is more accurate for spectrum clustering of communication big data and improves clustering efficiency.

关键词： wavelet analysis network communication big data spectral clustering feature extraction clustering efficiency fuzzy c-means clustering method wavelet decomposition method

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Track-Pattern-Based characteristics of Extratropical Transitioning Tropical cyclones in the Western North Pacific

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Advances in Atmospheric Sciences 2024年第6期41卷 1251-1263页

作者： Hong HUANG Dan WU Yuan WANG Zhen WANG Yu LIU College of Meteorology and Oceanography National University of Defense TechnologyChangsha 410000China No.31631 Army of PLA Huizhou 516000China School of Atmospheric Sciences and Key Laboratory of Mesoscale Severe Weather/Ministry of Education Nanjing 210023China No.93721 Army of PLA Shuozhou 038300China

Based on the Regional Specialized Meteorological center(RSMc)Tokyo-Typhoon center best-track data and the NcEP-NcAR reanalysis dataset,extratropical transitioning(ET)tropical cyclones(ETcs)over the western North Pacific(WNP)during 1951–2021 are classified into six clusters using the fuzzy c-means clustering method(FcM)according to their track *** characteristics of the six hard-clustered ETcs with the highest membership coefficient are *** tropical cyclones(Tcs)that were assigned to clusters c2,c5,and c6 made landfall over eastern Asian countries,which severely threatened these *** landfalling Tcs,93.2%completed their ET after landfall,whereas 39.8%of ETcs completed their transition within one *** frequency of ETcs over the WNP has decreased in the past four decades,wherein cluster c5 demonstrated a significant decrease on both interannual and interdecadal timescales with the expansion and intensification of the western Pacific subtropical high(WPSH).This large-scale circulation pattern is favorable for c2 and causes it to become the dominant track pattern,owning to it containing the largest number of intensifying ETcs among the six clusters,a number that has increased insignificantly over the past four *** surface roughness variation and three-dimensional background circulation led to c5 containing the maximum number of landfalling Tcs and a minimum number of intensifying *** results will facilitate a better understanding of the spatiotemporal distributions of ET events and associated environment background fields,which will benefit the effective monitoring of these events over the WNP.

关键词： Western North Pacific tropical cyclone extratropical transition fuzzy c-means clustering method

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Spinning reserve scheduling in power systems containing wind and solar generations

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ELEcTRIcAL ENGINEERING 2021年第5期103卷 2507-2526页

作者： Ghaedi, Amir Gorginpour, Hamed Islamic Azad Univ Dept Elect Engn Dariun Branch Dariun Iran Persian Gulf Univ Engn Dept Mahini AvePOB *** Bushehr Iran

Wind and solar as two renewable energy resources are largely used to generate clean and sustainable energy in the power systems. To integrate these renewable energies in the power system, different aspects of the power system such as reliability and operation are affected that must be investigated. It is due to the variation in the generated power of these resources that are arisen from the variation in the wind speed and solar radiation. To model the uncertainty nature of large-scale wind and photovoltaic farms in the operation studies of the power system, an appropriate multistate reliability model is developed for the wind and photovoltaic farms considering both failure of main components and variation in the wind speed and solar radiation. To determine the optimum number of states in the reliability model of wind and photovoltaic farms, XB index is calculated and based on the fuzzy c-means clustering technique the transition rates among different states are obtained. To calculate the transition rates among different states, the fuzzy numbers are used that results in the accurate and reduced reliability model for wind and photovoltaic farms. To determine the probability of different states in the reliability model of wind and photovoltaic farms in the operation studies, matrix multiplication technique is utilized to determine important indices of the power system such as the unit commitment risk. In this paper, the amount of required spinning reserve of a power system containing wind and solar generation units can be determined based on the reliability criterion. The proposed technique is applied to the two reliability test systems including RBTS and IEEE-RTS and the reliability-based operation indices of these systems are calculated to present the effectiveness of the proposed analytical method.

关键词： Operating studies Photovoltaic (PV) farm Spinning reserve Uncertainty Wind farm XB index fuzzy c-means clustering method

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A data-driven study for evaluating the compressive strength of high-strength concrete

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INTERNATIONAL JOURNAL OF MAcHINE LEARNING AND cYBERNETIcS 2021年第12期12卷 3585-3595页

作者： Wei, Yufeng Han, Aiguo Xue, Xinhua Chengdu Univ Technol State Key Lab Geohazard Prevent & Geoenvironm Pro Chengdu Peoples R China Sichuan Univ Coll Water Resource & Hydropower Chengdu 610065 Peoples R China

To estimate the compressive strength of high-strength concrete (HSc), a hybrid model integrating the firefly algorithm (FFA) and fuzzy c-means (FcM) clustering method into the adaptive neuro fuzzy inference system (ANFIS) was developed in this paper. The FFA and FcM techniques were utilized to improve the forecasting accuracy of the proposed ANFIS. To establish the hybrid ANFIS-FFA model, five main constituents of HSc, cement, water, fine and coarse aggregates, and superplasticizer, are considered the input variables, and the compressive strength of HSc is used as the output variable. A comparison was conducted among four artificial intelligence models, including the proposed ANFIS-FFA model, the traditional ANFIS, the back propagation neural network (BPNN) and the extreme learning machine (ELM), in terms of four statistical indices. In addition, a detailed parametric study was conducted to investigate the influence of each input variable on the compressive strength of HSc. The results showed that the developed ANFIS-FFA model exhibits greater accuracy than the other three models, with a higher correlation coefficient (R) and lower root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) values, and it has great potential to accurately estimate the compressive strength of HSc.

关键词： Firefly algorithm Adaptive neuro fuzzy inference system compressive strength fuzzy c-means clustering method High-strength concrete

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Recognition of Lung Nodules in computerized Tomography Lung Images using a Hybrid method with class Imbalance Reduction

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INTERNATIONAL JOURNAL OF ADVANcED cOMPUTER ScIENcE AND APPLIcATIONS 2023年第5期14卷 431-444页

作者： Wang, Yingqiang Wang, Honggang Dong, Erqiang Xian Siyuan Univ Xian Sch Elect Informat Engn Xian 710038 Shaanxi Peoples R China

Lung cancer is among the deadly diseases affecting millions globally every year. Physicians' and radiologists' manual detection of lung nodules has low efficiency due to the variety of shapes and nodule locations. The paper aims to recognize the lung nodules in computerized tomography (cT) lung images utilizing a hybrid method to reduce the problem space at every step. First, the suggested method uses the fast and robust fuzzy c -means clustering method (FRFcM) algorithm to segment cT images and extract two lungs, followed by a convolutional neural network (cNN) to identify the sick lung for use in the next step. Then, the adaptive thresholding method detects the suspected regions of interest (ROIs) among all available objects in the sick lung. Next, some statistical features are selected from every ROI, and then a restricted Boltzmann machine (RBM) is considered a feature extractor that extracts rich features among the selected features. After that, an artificial neural network (ANN) is employed to classify ROIs and determine whether the ROI includes nodules or non-nodules. Finally, cancerous ROIs are localized by the Otsu thresholding algorithm. Naturally, sick ROIs are more than healthy ones, leading to a class imbalance that substantially decreases ANN ability. To solve this problem, a reinforcement learning (RL)-based algorithm is used, in which the states are sampled. The agent receives a larger reward/penalty for correct/incorrect classification of the examples related to the minority class. The proposed model is compared with state-of-the-art methods on the lung image database consortium image collection (LIDc-IDRI) dataset and standard performance metrics. The results of the experiments demonstrate that the proposed model outperforms its rivals.

关键词： Lung cancer artificial neural network fuzzy c-means clustering method reinforcement learning restricted boltzmann machine

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The efficiency analysis of world top container ports using two-stage uncertainty DEA model and FcM

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MARITIME BUSINESS REVIEW 2021年第1期6卷 2-21页

作者： Thi Quynh Mai Pham Park, Gyci Kark choi, Kyoung-Hoon Mokpo Natl Maritime Univ Dept Int Transportat Syst Mokpo South Korea Vietnam Maritime Univ Dept Econ Haiphong Vietnam Mokpo Natl Maritime Univ Mokpo South Korea

Purpose - The purpose of this paper is to present an integrated model to measure the operational efficiency of the top 40 container ports in the world for a five-year continuous period using a two-stage uncertainty data envelopment analysis (UDEA) combined with fuzzy c-means clustering method (FcM). Design/methodology/approach - UDEA model is adopted for measuring the efficiency of container ports to overcome the limitation of the basic model, which is unable to handle uncertain data that are easy to meet in practice. FcM algorithm is implemented to find similar distribution efficiency scores of two stages and the cluster similar efficiency scores of container ports into various groups. Findings - The combination of the two-stage UDEA model and the FcM algorithm provided a more comprehensive view when evaluating the performance of container ports. The UDEA results show that most of the container ports have reduced their profitability level in the second stage and most of the efficient container ports have turned into inefficient ones because of their small scale. Originality/value - This paper proposes using the two-stage UDEA model to evaluate port efficiency based on two main aspects of productivity and profitability. Moreover, it combines DEA and FcM algorithms to offer a more comprehensive view when measuring the performance of container ports.

关键词： container ports Operational efficiency Two-stage uncertainty data envelopment analysis fuzzy c-means clustering method

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Prediction of the bond strength of FRP-to-concrete under direct tension by AcO-based ANFIS approach

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cOMPOSITE STRUcTURES 2022年 282卷

作者： Pei, Zuan Wei, Yufeng Chengdu Univ Technol State Key Lab Geohazard Prevent & Geoenvironm Chengdu Peoples R China

In this study, a hybrid model integrating the ant colony optimization (AcO) algorithm and fuzzy c-means (FcM) clustering method into the adaptive neuro-fuzzy inference system (ANFIS) was proposed to predict the bond strength between fibre-reinforced polymer (FRP) sheets and concrete surface under direct tension. Eight parameters including the compressive strength of concrete, maximum aggregate size, tensile strength of FRP, thickness of FRP, elastic modulus of FRP, adhesive tensile strength, length of FRP and width of FRP are employed as the inputs, and the bond strength is used as the output variable. A comparison was conducted between some existing empirical models and the proposed hybrid AcO-based ANFIS model. The results confirmed that the developed AcO-based ANFIS model exhibits greater accuracy than the other eleven models, with higher coefficient of determination (R2 = 0.97) and Nash-Sutcliffe efficiency index (NS = 0.97), and lower root mean squared error (RMSE = 1.29 kN), mean absolute error (MAE = 0.81 kN) and mean absolute relative error (MARE = 0.053), while according to the Akaike information criterion (AIc) index, the accuracy of this model lies in its considerable complexity compared to others.

关键词： Fibre-reinforced polymer Adaptive neuro-fuzzy inference system Ant colony optimization algorithm fuzzy c-means clustering method

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A cell-based dosimetry model for radium-223 dichloride therapy using bone micro-cT images and GATE simulations

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PHYSIcS IN MEDIcINE AND BIOLOGY 2020年第4期65卷 045010-045010页

作者： Pinto, Gabriella M. Bonifacio, Daniel A. B. de Sa, Lidia, V Lima, Luis Felipe c. Vieira, Igor F. Lopes, Ricardo T. Univ Fed Rio de Janeiro Nucl Instrumentat Lab PEN COPPE Rio De Janeiro RJ Brazil Inst Radioprotect & Dosimetry IRD CNEN Dept Med Phys Rio De Janeiro RJ Brazil Univ Hosp Dept Imaging & Pathol Nucl Med & Mol Imaging Herestr 49 B-3000 Leuven Belgium Katholieke Univ Leuven Herestr 49 B-3000 Leuven Belgium

Dosimetry at the cellular level has outperformed macrodosimetry in terms of agreement with toxicity effects in clinical studies. This fact has encouraged dosimetry studies aiming to quantify the absorbed doses needed to reach radiotoxicity at the cellular level and to inform recommendations on the administration of radium-223. The aim of this work is to qualitatively and quantitatively evaluate the absorbed doses of radium-223 and the interactions of the doses at the cellular level. The analysis was performed by Monte carlo simulations in GATE using micro-cT image of a mouse. Two physics lists available in the GATE code were tested. The influence of single and multiple scattering models on the absorbed dose distribution and number of particle hits was also studied. In addition, the fuzzy c-means clustering method was used for data segmentation. The segmentation method was suitable for these analyses, particularly given that it was unsupervised. There was no significant difference in the estimated absorbed dose between the two proposed physics lists. The absorbed dose values were not significantly influenced by scattering, although single scattering resulted in twice as many interactions as multiple scattering. The absorbed dose histogram at the voxel level shows heterogeneous absorbed dose values within each shell, but the observations from the graph of the medians were comparable to those in the literature. The interaction histogram indicates 104 events, although some voxels had no interactions with alpha particles. However, the voxels did not show absorbed doses capable of deterministic effects in the deepest part of the bone marrow. The absorbed dose distribution in images of mouse trabecular bone was compatible with simple geometric models, with absorbed doses capable of deterministic effects near the bone surface. The interaction distributions need to be correlated with in vivo studies for better interpretation.

关键词： cell-based dosimetry radium-223 therapy skeletal dosimetry fuzzy c-means clustering method

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